Brain Storm Optimization Model Based on Uncertainty Information

摘要

Brain storm optimization is a new swarm intelligence, which mimics the human brainstorming process. In this paper, a modified brain storm optimization is proposed based on uncertainty information. It adopts affinity propagation clustering instead of k-means clustering. Meanwhile, a creating operator combining the information of multiple clusters is introduced by borrowing the idea of cloud drops algorithm. The proposed brain storm optimization is characterized by mining and utilizing the uncertain information of candidate solutions with no need for the number of clusters. Finally, the modified brain storm optimization is applied to numerical optimization. The simulation results show that the proposed algorithm has better optimization results and higher rate of success than the original version.